We used R version 3.6.3 (2020-02-29) and the R-packages tidyverse (version 1.3.0), here (version 0.1), brms (version 2.12.0), modelr (version0.1.6), tidybayes (version 2.0.1), and patchwork (version 1.0.0) for data preparation and analysis.
We collected data from a total of XX participants. After exclusions based on non-completion or missing data (details to be expanded) we analysed data from 159 participants (age M = 32.65, SD = 8.96, Range = 19, `72). On average participants took 1409.9 seconds to complete the task (SD = 3463.64).
Exclusions: details to be expanded.
The below graph shows the number of participats in a given employment situation during lockdown.
The below graph shows the number of participants affected by differing lockdown situations.
The below graph shows the number of participants affected by in a given living situation.
We took the data from the DAS questionnaire ratings before and after lockdown and combined these with hours played before and after lockdown.
Given the data are generated from three Likert-style questionnaire responses per subscale, added together and multiplied by two, responses are thus strictly positive integers. This required fitting the data to cumulative models using a logit link function.
We fitted these models separately for each subscale of the DAS using the brm function in brms, estimating the effect of hours played in video games, time (pre- and post-lockdown), and the interaction between them. The effect of time was sum-coded (i.e. before = 1 and after = -1), such that the fixed effect of time represents the average effect of time across the range of hours played (i.e. a main effect). All models contained random intercepts per participant. Models used a \(Normal(0, 50)\) prior on the intercept, a \(Normal(0, 5)\) prior on the slope terms, and an \(Exponential(1)\) prior on the standard deviation term.
We present the fixed effect parameter estimates below on the log scale followed by the same parameter estimates backtransformed to the natural (i.e. rating) scale. For each model, we present posterior predictions from the model. We evaluate the evidence in support for an effect for each fixed factor using Bayes factors calculated using the Savage Savage-Dickey density ratio with the hypothesis function in brms.
Below we show spaghetti plots showing the predictions for each model for the effect of hours played within each subscale and time period on DAS outcomes.
Spaghetti Plots showing Draws from the Posterior for the Fixed Effect of Hours Played DAS outcomes Before and After Lockdown (lines). Dots represent observed data.
On the logit scale, model fixed effects parameter estimates are as follows:
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept[1] | -4.60 | 0.58 | -5.79 | -3.54 | 1 | 1303 | 2397 |
| Intercept[2] | -3.06 | 0.50 | -4.08 | -2.11 | 1 | 1307 | 2240 |
| Intercept[3] | -2.26 | 0.49 | -3.25 | -1.33 | 1 | 1311 | 2271 |
| Intercept[4] | -1.65 | 0.48 | -2.61 | -0.73 | 1 | 1356 | 2374 |
| Intercept[5] | -0.90 | 0.46 | -1.84 | 0.01 | 1 | 1385 | 2322 |
| Intercept[6] | 0.02 | 0.46 | -0.91 | 0.91 | 1 | 1437 | 2456 |
| Intercept[7] | 0.50 | 0.46 | -0.40 | 1.40 | 1 | 1495 | 2442 |
| Intercept[8] | 1.06 | 0.46 | 0.15 | 1.98 | 1 | 1576 | 2580 |
| Intercept[9] | 1.65 | 0.47 | 0.74 | 2.57 | 1 | 1636 | 2743 |
| Intercept[10] | 2.20 | 0.47 | 1.26 | 3.12 | 1 | 1669 | 2920 |
| Intercept[11] | 2.60 | 0.48 | 1.67 | 3.55 | 1 | 1727 | 2944 |
| Intercept[12] | 3.11 | 0.50 | 2.13 | 4.09 | 1 | 1808 | 2776 |
| Intercept[13] | 3.45 | 0.51 | 2.48 | 4.44 | 1 | 1821 | 2993 |
| Intercept[14] | 3.82 | 0.52 | 2.82 | 4.84 | 1 | 1806 | 2859 |
| Intercept[15] | 4.21 | 0.53 | 3.21 | 5.28 | 1 | 1844 | 2903 |
| Intercept[16] | 5.12 | 0.56 | 4.05 | 6.26 | 1 | 1975 | 3195 |
| Intercept[17] | 5.58 | 0.58 | 4.49 | 6.74 | 1 | 2060 | 2992 |
| Intercept[18] | 6.24 | 0.62 | 5.07 | 7.47 | 1 | 2151 | 2866 |
| Intercept[19] | 6.83 | 0.65 | 5.62 | 8.14 | 1 | 2266 | 2879 |
| Intercept[20] | 7.68 | 0.72 | 6.30 | 9.15 | 1 | 2497 | 2954 |
| Intercept[21] | 8.65 | 0.82 | 7.12 | 10.35 | 1 | 2823 | 2978 |
| time1 | -0.76 | 0.21 | -1.17 | -0.35 | 1 | 3778 | 3010 |
| total_hours | 0.02 | 0.01 | 0.00 | 0.05 | 1 | 1921 | 2987 |
| time1:total_hours | 0.01 | 0.01 | 0.00 | 0.03 | 1 | 3663 | 3245 |
On the natural scale, model fixed effects parameter estimates are as follows:
## Parameter Estimate Est.Error Q2.5 Q97.5
## 1 b_Intercept[1] 0.012 0.00673 0.0031 0.028
## 2 b_Intercept[2] 0.050 0.02404 0.0166 0.108
## 3 b_Intercept[3] 0.103 0.04431 0.0374 0.210
## 4 b_Intercept[4] 0.172 0.06577 0.0682 0.325
## 5 b_Intercept[5] 0.297 0.09305 0.1365 0.502
## 6 b_Intercept[6] 0.504 0.10907 0.2875 0.713
## 7 b_Intercept[7] 0.618 0.10352 0.4011 0.803
## 8 b_Intercept[8] 0.733 0.08785 0.5376 0.878
## 9 b_Intercept[9] 0.829 0.06543 0.6766 0.929
## 10 b_Intercept[10] 0.892 0.04602 0.7794 0.958
## 11 b_Intercept[11] 0.925 0.03397 0.8421 0.972
## 12 b_Intercept[12] 0.952 0.02327 0.8942 0.984
## 13 b_Intercept[13] 0.966 0.01741 0.9225 0.988
## 14 b_Intercept[14] 0.976 0.01267 0.9437 0.992
## 15 b_Intercept[15] 0.983 0.00898 0.9611 0.995
## 16 b_Intercept[16] 0.993 0.00402 0.9829 0.998
## 17 b_Intercept[17] 0.996 0.00265 0.9889 0.999
## 18 b_Intercept[18] 0.998 0.00151 0.9937 0.999
## 19 b_Intercept[19] 0.999 0.00091 0.9964 1.000
## 20 b_Intercept[20] 0.999 0.00046 0.9982 1.000
## 21 b_Intercept[21] 1.000 0.00021 0.9992 1.000
## 22 b_time1 -0.759 0.21073 -1.1720 -0.347
## 23 b_total_hours 0.022 0.01323 -0.0042 0.048
## 24 b_time1:total_hours 0.011 0.00759 -0.0039 0.026
We calculated Bayes factors to evaluate the strength of evidence in support of a null effect the fixed effects of total hours played and time (pre- and post-lockdown) and their interaction.
## # A tibble: 3 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 (time1) = 0 -0.759 0.211 -1.17 -0.347 0.0205 0.0201 *
## 2 (total_hours)… 0.0216 0.0132 -0.00421 0.0476 99.9 0.990 <NA>
## 3 (time1:total_… 0.0110 0.00759 -0.00392 0.0259 233. 0.996 <NA>
We found overwhelming evidence in support of the null model in comparison to the alternative for the main effect of total hours played and the interaction between total hours played (both BF01 >=99). However, we found strong evidence in support of a main effect of time on depression, BF10= 48.75. This supports the notion that post-lockdown measures of depression were higher than pre-lockdown measures.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept[1] | -3.20 | 0.53 | -4.29 | -2.25 | 1 | 1417 | 2031 |
| Intercept[2] | -1.16 | 0.46 | -2.09 | -0.28 | 1 | 1675 | 2235 |
| Intercept[3] | 0.00 | 0.45 | -0.90 | 0.88 | 1 | 1850 | 2491 |
| Intercept[4] | 0.70 | 0.46 | -0.19 | 1.59 | 1 | 1924 | 2521 |
| Intercept[5] | 1.71 | 0.46 | 0.81 | 2.60 | 1 | 2038 | 2776 |
| Intercept[6] | 2.49 | 0.47 | 1.57 | 3.43 | 1 | 1970 | 2505 |
| Intercept[7] | 3.26 | 0.49 | 2.32 | 4.24 | 1 | 2032 | 2624 |
| Intercept[8] | 4.04 | 0.52 | 3.03 | 5.09 | 1 | 2158 | 2724 |
| Intercept[9] | 4.86 | 0.56 | 3.83 | 5.97 | 1 | 2177 | 2595 |
| Intercept[10] | 5.90 | 0.61 | 4.74 | 7.14 | 1 | 2269 | 2736 |
| Intercept[11] | 7.04 | 0.68 | 5.74 | 8.40 | 1 | 2461 | 2554 |
| Intercept[12] | 7.35 | 0.70 | 6.03 | 8.76 | 1 | 2538 | 2875 |
| Intercept[13] | 7.84 | 0.73 | 6.45 | 9.35 | 1 | 2521 | 2946 |
| Intercept[14] | 8.65 | 0.81 | 7.14 | 10.25 | 1 | 2754 | 2988 |
| Intercept[15] | 9.57 | 0.92 | 7.87 | 11.50 | 1 | 3081 | 2996 |
| Intercept[16] | 11.16 | 1.22 | 8.99 | 13.76 | 1 | 3740 | 3189 |
| Intercept[17] | 12.81 | 1.63 | 9.99 | 16.44 | 1 | 3930 | 3280 |
| Intercept[18] | 24.76 | 10.28 | 12.88 | 52.15 | 1 | 5219 | 3329 |
| Intercept[19] | 37.63 | 14.96 | 16.79 | 72.59 | 1 | 4500 | 3157 |
| Intercept[20] | 54.08 | 19.56 | 23.08 | 99.39 | 1 | 4660 | 2849 |
| Intercept[21] | 80.05 | 27.47 | 35.67 | 139.57 | 1 | 4941 | 3363 |
| time1 | -0.18 | 0.21 | -0.59 | 0.23 | 1 | 4144 | 2859 |
| total_hours | 0.03 | 0.01 | 0.00 | 0.05 | 1 | 1795 | 2500 |
| time1:total_hours | 0.00 | 0.01 | -0.01 | 0.02 | 1 | 3425 | 2931 |
On the natural scale, model fixed effects parameter estimates are as follows:
| Parameter | Estimate | Est.Error | Q2.5 | Q97.5 |
|---|---|---|---|---|
| b_Intercept[1] | 0.04 | 0.02 | 0.01 | 0.10 |
| b_Intercept[2] | 0.25 | 0.08 | 0.11 | 0.43 |
| b_Intercept[3] | 0.50 | 0.11 | 0.29 | 0.71 |
| b_Intercept[4] | 0.66 | 0.10 | 0.45 | 0.83 |
| b_Intercept[5] | 0.84 | 0.06 | 0.69 | 0.93 |
| b_Intercept[6] | 0.92 | 0.04 | 0.83 | 0.97 |
| b_Intercept[7] | 0.96 | 0.02 | 0.91 | 0.99 |
| b_Intercept[8] | 0.98 | 0.01 | 0.95 | 0.99 |
| b_Intercept[9] | 0.99 | 0.01 | 0.98 | 1.00 |
| b_Intercept[10] | 1.00 | 0.00 | 0.99 | 1.00 |
| b_Intercept[11] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[12] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[13] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[14] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[15] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[16] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[17] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[18] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[19] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[20] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[21] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_time1 | -0.18 | 0.21 | -0.59 | 0.23 |
| b_total_hours | 0.03 | 0.01 | 0.00 | 0.05 |
| b_time1:total_hours | 0.00 | 0.01 | -0.01 | 0.02 |
We calculated Bayes factors to evaluate the strength of evidence in support of a null effect the fixed effects of total hours played and time (pre- and post-lockdown) and their interaction.
## # A tibble: 3 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <chr>
## 1 (time1) = 0 -0.183 0.207 -5.92e-1 0.232 16.9 0.944 <NA>
## 2 (total_hours)… 0.0259 0.0130 7.95e-4 0.0518 50.8 0.981 *
## 3 (time1:total_… 0.00271 0.00737 -1.17e-2 0.0176 612. 0.998 <NA>
We found overwhelming evidence in support of the null model in comparison to the alternative for all main effects and interactions (all BF01 >= 16). Thus, the data are more likely under the null than the alternative model.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept[1] | -5.05 | 0.57 | -6.23 | -3.98 | 1 | 1634 | 2406 |
| Intercept[2] | -4.06 | 0.53 | -5.15 | -3.07 | 1 | 1568 | 2390 |
| Intercept[3] | -3.27 | 0.49 | -4.26 | -2.36 | 1 | 1582 | 2431 |
| Intercept[4] | -2.55 | 0.46 | -3.46 | -1.68 | 1 | 1534 | 2287 |
| Intercept[5] | -1.65 | 0.44 | -2.53 | -0.83 | 1 | 1556 | 2414 |
| Intercept[6] | -0.89 | 0.43 | -1.77 | -0.09 | 1 | 1571 | 2457 |
| Intercept[7] | -0.12 | 0.42 | -0.97 | 0.69 | 1 | 1606 | 2054 |
| Intercept[8] | 0.47 | 0.42 | -0.38 | 1.27 | 1 | 1693 | 2502 |
| Intercept[9] | 1.07 | 0.43 | 0.21 | 1.89 | 1 | 1750 | 2200 |
| Intercept[10] | 1.84 | 0.44 | 0.95 | 2.69 | 1 | 1871 | 2348 |
| Intercept[11] | 2.57 | 0.45 | 1.68 | 3.45 | 1 | 2026 | 2237 |
| Intercept[12] | 3.03 | 0.46 | 2.12 | 3.91 | 1 | 2117 | 2651 |
| Intercept[13] | 3.56 | 0.47 | 2.64 | 4.48 | 1 | 2046 | 2699 |
| Intercept[14] | 4.15 | 0.50 | 3.18 | 5.15 | 1 | 2159 | 3004 |
| Intercept[15] | 4.85 | 0.53 | 3.83 | 5.89 | 1 | 2296 | 3287 |
| Intercept[16] | 5.33 | 0.56 | 4.27 | 6.47 | 1 | 2524 | 3266 |
| Intercept[17] | 5.66 | 0.58 | 4.53 | 6.83 | 1 | 2656 | 2955 |
| Intercept[18] | 6.75 | 0.66 | 5.51 | 8.05 | 1 | 2936 | 3366 |
| Intercept[19] | 8.13 | 0.83 | 6.63 | 9.81 | 1 | 3706 | 3363 |
| Intercept[20] | 9.32 | 1.06 | 7.47 | 11.61 | 1 | 4298 | 3419 |
| Intercept[21] | 10.90 | 1.56 | 8.29 | 14.47 | 1 | 5746 | 2827 |
| time1 | -0.28 | 0.20 | -0.67 | 0.10 | 1 | 4773 | 3066 |
| total_hours | 0.00 | 0.01 | -0.02 | 0.02 | 1 | 2157 | 2785 |
| time1:total_hours | -0.01 | 0.01 | -0.02 | 0.01 | 1 | 4546 | 3056 |
On the natural scale, model fixed effects parameter estimates are as follows:
| Parameter | Estimate | Est.Error | Q2.5 | Q97.5 |
|---|---|---|---|---|
| b_Intercept[1] | 0.01 | 0.00 | 0.00 | 0.02 |
| b_Intercept[2] | 0.02 | 0.01 | 0.01 | 0.04 |
| b_Intercept[3] | 0.04 | 0.02 | 0.01 | 0.09 |
| b_Intercept[4] | 0.08 | 0.03 | 0.03 | 0.16 |
| b_Intercept[5] | 0.17 | 0.06 | 0.07 | 0.30 |
| b_Intercept[6] | 0.30 | 0.09 | 0.15 | 0.48 |
| b_Intercept[7] | 0.47 | 0.10 | 0.28 | 0.66 |
| b_Intercept[8] | 0.61 | 0.10 | 0.41 | 0.78 |
| b_Intercept[9] | 0.74 | 0.08 | 0.55 | 0.87 |
| b_Intercept[10] | 0.85 | 0.05 | 0.72 | 0.94 |
| b_Intercept[11] | 0.92 | 0.03 | 0.84 | 0.97 |
| b_Intercept[12] | 0.95 | 0.02 | 0.89 | 0.98 |
| b_Intercept[13] | 0.97 | 0.01 | 0.93 | 0.99 |
| b_Intercept[14] | 0.98 | 0.01 | 0.96 | 0.99 |
| b_Intercept[15] | 0.99 | 0.00 | 0.98 | 1.00 |
| b_Intercept[16] | 0.99 | 0.00 | 0.99 | 1.00 |
| b_Intercept[17] | 1.00 | 0.00 | 0.99 | 1.00 |
| b_Intercept[18] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[19] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[20] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_Intercept[21] | 1.00 | 0.00 | 1.00 | 1.00 |
| b_time1 | -0.28 | 0.20 | -0.67 | 0.10 |
| b_total_hours | 0.00 | 0.01 | -0.02 | 0.02 |
| b_time1:total_hours | -0.01 | 0.01 | -0.02 | 0.01 |
We calculated Bayes factors to evaluate the strength of evidence in support of a null effect the fixed effects of total hours played and time (pre- and post-lockdown) and their interaction.
## # A tibble: 3 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (time1) = 0 -2.80e-1 0.197 -0.671 0.0968 9.79 0.907 NA
## 2 (total_hours)… -2.80e-4 0.0125 -0.0244 0.0238 407. 0.998 NA
## 3 (time1:total_… -7.04e-3 0.00731 -0.0214 0.00708 394. 0.997 NA
Here, we found storng evidence against an effect of time on stress (BF01 = 9.79), and overwhelming evidence against an effect of total hours played and the interaction between time and total hours played on stress (both BF01 > 394).
We next explored the effect of the change in total hours playing games before and after lockdown on the difference in mental health outcomes pre- and post-lockdown. Here, hours played after were subtracted from hours played before, and DAS outcomes after were (separately) subtracted from DAS outcomes before.
Models were again fitted separately for each subscale in brms using the brm function. Here, the data were fitted using a Gaussian model (identity link function), with the fixed effect of difference in hours played. Models used a \(Normal(0, 5)\) prior on the intercept, a \(Normal(0, 1)\) prior on the slope term, and an \(Exponential(1)\) prior on the sigma term.
Again, the presence of an effect of difference in hours played was evaluated using Bayes factors calculated using the Savage-Dickey density ratio. We also present the fixed effects for these models on the natural scale.
We present posterior predictions for the effect of difference in hours played pre- and post-lockdown on differences in outcomes for each subscale. Here, lines represent the posterior median along with 50%, 80%, and 95% credible intervals (shaded).
Posterior Predictions for the Fixed Effect of Difference in Hours Played Pre- and Post-Lockdown on Change in DAS Outcomes. Lines represent the posterior median and 50%, 80%, and 95% Credible Intervals.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept | -1.7 | 0.46 | -2.61 | -0.82 | 1 | 3744 | 2889 |
| hours_diff | 0.0 | 0.03 | -0.05 | 0.05 | 1 | 3928 | 3093 |
## # A tibble: 1 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (hours_diff) … 0.00265 0.0253 -0.0465 0.0519 38.7 0.975 NA
We found overwhelming evidence in support of the null model in comparison to the alternative for the main effect of difference in hours played pre- and post-lockdown and the difference in depression scores.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept | -0.6 | 0.28 | -1.14 | -0.03 | 1 | 4604 | 3150 |
| hours_diff | 0.0 | 0.02 | -0.03 | 0.03 | 1 | 5147 | 3074 |
## # A tibble: 1 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (hours_diff) … 0.00398 0.0157 -0.0265 0.0347 60.7 0.984 NA
We found overwhelming evidence in support of the null model in comparison to the alternative for the main effect of difference in hours played pre- and post-lockdown and the difference in anxiety scores.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept | -1.51 | 0.39 | -2.29 | -0.72 | 1 | 4005 | 3014 |
| hours_diff | -0.01 | 0.02 | -0.05 | 0.03 | 1 | 3914 | 3006 |
## # A tibble: 1 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (hours_diff) … -0.0110 0.0217 -0.0522 0.0327 39.9 0.976 NA
We found overwhelming evidence in support of the null model in comparison to the alternative for the main effect of difference in hours played pre- and post-lockdown and the difference in stress scores.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept | -2.02 | 0.69 | -3.37 | -0.65 | 1 | 4251 | 3033 |
| total_hours_after | 0.01 | 0.02 | -0.03 | 0.05 | 1 | 4412 | 3172 |
## # A tibble: 1 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (total_hours_… 0.00915 0.0187 -0.0274 0.0460 45.2 0.978 NA
We found overwhelming evidence in support of the null model in comparison to the alternative for the main effect of total hours played during lockdown and the difference in depression scores.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept | -0.64 | 0.42 | -1.48 | 0.14 | 1 | 3868 | 2737 |
| total_hours_after | 0.00 | 0.01 | -0.02 | 0.02 | 1 | 3964 | 2919 |
## # A tibble: 1 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (total_hours_… -1.89e-4 0.0116 -0.0223 0.0217 84.7 0.988 NA
We found overwhelming evidence in support of the null model in comparison to the alternative for the main effect of total hours played during lockdown and the difference in anxiety scores.
| Estimate | Est.Error | l-95% CI | u-95% CI | Rhat | Bulk_ESS | Tail_ESS | |
|---|---|---|---|---|---|---|---|
| Intercept | -1.07 | 0.58 | -2.21 | 0.04 | 1 | 4589 | 2986 |
| total_hours_after | -0.01 | 0.02 | -0.04 | 0.02 | 1 | 4571 | 3060 |
## # A tibble: 1 x 8
## Hypothesis Estimate Est.Error CI.Lower CI.Upper Evid.Ratio Post.Prob Star
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
## 1 (total_hours_… -0.00986 0.0161 -0.0413 0.0211 49.9 0.980 NA
We found overwhelming evidence in support of the null model in comparison to the alternative for the main effect of total hours played during lockdown and the difference in stress scores.
Posterior predictive checks were carried out for all fitted models. These show a relatively good fit for each model whereby draws from the posterior are close to the fitted data. This indicates good model fit.
Posterior Predictive Check for the Effect of Hours Played Before and After Lockdown on Mental Health Outcomes for Depression
Posterior Predictive Check for the Effect of Difference in Hours Played Before and After Lockdown on Differences in Mental Health Outcomes for Depression
Posterior Predictive Check for the Effect of Hours Played Before and After Lockdown on Mental Health Outcomes for Anxiety